Tom and Nate catch up on the rapidly evolving (and political) space of AI regulation. We cover CA SB 1047, recent policing of data scraping, presidential appointees, antitrust intention vs. implementation, FLOP thresholds, and everything else touching the future of large ML models.
Nate's internet cut out, so this episode ends a little abruptly. Reach out with any questions to mail at retortai.com
Some links: - night falls on the cumberlands https://en.wikipedia.org/wiki/Night_Comes_to_the_Cumberlands - hillbilly elegy https://en.wikipedia.org/wiki/Hillbilly_Elegy - wired piece on data https://www.wired.com/story/youtube-training-data-apple-nvidia-anthropic/ - nate's recent piece on AI regulation https://www.interconnects.ai/p/sb-1047-and-open-weights
00:00  Intro  01:19 Training Data and the Media  03:43 Norms, Power, and the Limits of Regulation 08:52 OpenAI's Business Model 12:33  Antitrust: The Essential Tool for Governing AI 17:11 Users as Afterthoughts 20:07 Depoliticizing AI  26:14  “Breaking Bad” & the AI Parallel 28:11  The “Little Tech” Agenda 31:03  Reframing the Narrative of Big Tech   32:20  “The Lean Startup” & AI's Uncertainty

Thomas Krendl Gilbert

Nathan Lambert

What we are getting wrong about AI regulation

Tom and Nate catch up on the rapidly evolving (and political) space of AI regulation. We cover CA SB 1047, recent policing of data scraping, presidential appointees, antitrust intention vs. implementation, FLOP thresholds, and everything else touching the future of large ML models. Nate's internet cut out, so this episode ends a little abruptly. Reach [&hellip;]

The Retort AI Podcast Episode #31

What we are getting wrong about AI regulation